{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PP3CJZ3FP2QOFG6Q7MDMZE7PUD","short_pith_number":"pith:PP3CJZ3F","canonical_record":{"source":{"id":"2604.08581","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-28T20:15:50Z","cross_cats_sorted":[],"title_canon_sha256":"cf92076f4c65e72a79ad833d692fb3d80fb87fea233987d56bd26871b21ec01f","abstract_canon_sha256":"e80e9c38142e9ff7899e66cf87ab20aeaf86a1ef5ae795a8ec765155fff7621f"},"schema_version":"1.0"},"canonical_sha256":"7bf624e7657ea0e29bd0fb06cc93efa0c76927e8562c41e2906ee5a8c6496358","source":{"kind":"arxiv","id":"2604.08581","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.08581","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"arxiv_version","alias_value":"2604.08581v1","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.08581","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"pith_short_12","alias_value":"PP3CJZ3FP2QO","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"pith_short_16","alias_value":"PP3CJZ3FP2QOFG6Q","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"pith_short_8","alias_value":"PP3CJZ3F","created_at":"2026-06-12T01:09:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PP3CJZ3FP2QOFG6Q7MDMZE7PUD","target":"record","payload":{"canonical_record":{"source":{"id":"2604.08581","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-28T20:15:50Z","cross_cats_sorted":[],"title_canon_sha256":"cf92076f4c65e72a79ad833d692fb3d80fb87fea233987d56bd26871b21ec01f","abstract_canon_sha256":"e80e9c38142e9ff7899e66cf87ab20aeaf86a1ef5ae795a8ec765155fff7621f"},"schema_version":"1.0"},"canonical_sha256":"7bf624e7657ea0e29bd0fb06cc93efa0c76927e8562c41e2906ee5a8c6496358","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:09:27.559191Z","signature_b64":"5TGxsjxkpq8AROetTzcOC+JL901VdS3mMwJ1isQ1EpbN1f8yMaO3jnX1ZdhCjIQkILPEPM+osZc3hXelgyqYCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bf624e7657ea0e29bd0fb06cc93efa0c76927e8562c41e2906ee5a8c6496358","last_reissued_at":"2026-06-12T01:09:27.558787Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:09:27.558787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.08581","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-12T01:09:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+/kGUKgploM7qqrF1XPRBzP1UEWNHOdEGzoRInQqS1cv0KTuWYUiZIIwpYifTqXTiDVp5PiRvKcE2LrVwt7QDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T12:32:15.794300Z"},"content_sha256":"f04d79dc4fc5812bfad77b3227733958f8eb007880efdfe4b7cd7e2197d5f908","schema_version":"1.0","event_id":"sha256:f04d79dc4fc5812bfad77b3227733958f8eb007880efdfe4b7cd7e2197d5f908"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PP3CJZ3FP2QOFG6Q7MDMZE7PUD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A Z-score TinyML system trains and detects appliance anomalies entirely on microcontrollers using power side-channel data with perfect accuracy and minimal resources.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Abdulrahman Albaiz, Fathi Amsaad","submitted_at":"2026-03-28T20:15:50Z","abstract_excerpt":"This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for real-time monitoring of appliance behavior using power side-channel data. Unlike existing Internet of Things (IoT) anomaly detection approaches that rely on offline training or cloud-assisted analytics, the proposed system performs both model training and inference directly on a resource-constrained microcontroller without external computation or connectivity. The system continuously samples current consumption, computes Root Mean Square (RMS)"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB SRAM and 63 KB Flash.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That power side-channel RMS values under controlled anomaly conditions in the 14-day mini-fridge dataset are representative of real-world anomalies and that Z-score thresholds derived from the training phase will generalize without overfitting or missing subtle deviations.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A Z-score TinyML system trains and detects appliance anomalies entirely on microcontrollers using power side-channel data with perfect accuracy and minimal resources.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"f91ed98faf4b2034ccf5c6c19bc815f227d38cb96395c16a7df2dc0aa6a63e46"},"source":{"id":"2604.08581","kind":"arxiv","version":1},"verdict":{"id":"1e41ad32-a0c2-4f1a-bd24-05389cb2d413","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T22:05:30.422912Z","strongest_claim":"Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB SRAM and 63 KB Flash.","one_line_summary":"A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That power side-channel RMS values under controlled anomaly conditions in the 14-day mini-fridge dataset are representative of real-world anomalies and that Z-score thresholds derived from the training phase will generalize without overfitting or missing subtle deviations.","pith_extraction_headline":"A Z-score TinyML system trains and detects appliance anomalies entirely on microcontrollers using power side-channel data with perfect accuracy and minimal resources."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.08581/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":15,"sample":[{"doi":"","year":2023,"title":"A Comprehensive Survey on TinyML","work_id":"612c03a2-e5b9-4a1e-9970-d23b577d88f0","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"TinyML -Enabled Frugal Smart Objects: Challenges and Opportunities","work_id":"5bf738a1-3d32-497a-9a2d-dbdfa3cb01ae","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2003,"title":"Benchmarking TinyML Systems: Challenges and Direction","work_id":"922e79c4-b1db-4146-9f0d-6904f483ea1d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Anomaly Detection in Smart Environments: A Comprehensive Survey","work_id":"49ab43d5-6541-4f12-8ced-6d5316474b79","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"Nonintrusive Appliance Load Monitoring: Review and Outlook","work_id":"c5e627f4-dc54-4021-bffe-82d46b35c360","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":15,"snapshot_sha256":"1d32ffb9c9d22e948bcc7aeaed598d0b66c783059f53c50701c12cf883c18851","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"1e41ad32-a0c2-4f1a-bd24-05389cb2d413"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-12T01:09:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yJY2uy2SEe7wQ2VN1utD88eMepUFuw0/xftfLmCOvX1gM+jsCNqUh0azA6LAG0CPIcVcM9rLLlV8gMAqRJuwCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T12:32:15.794858Z"},"content_sha256":"56111df5cc006f5417c50f2a2223f7752f4daf7ccb988bc664879c8887f07719","schema_version":"1.0","event_id":"sha256:56111df5cc006f5417c50f2a2223f7752f4daf7ccb988bc664879c8887f07719"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD/bundle.json","state_url":"https://pith.science/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-27T12:32:15Z","links":{"resolver":"https://pith.science/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD","bundle":"https://pith.science/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD/bundle.json","state":"https://pith.science/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PP3CJZ3FP2QOFG6Q7MDMZE7PUD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PP3CJZ3FP2QOFG6Q7MDMZE7PUD","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e80e9c38142e9ff7899e66cf87ab20aeaf86a1ef5ae795a8ec765155fff7621f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-28T20:15:50Z","title_canon_sha256":"cf92076f4c65e72a79ad833d692fb3d80fb87fea233987d56bd26871b21ec01f"},"schema_version":"1.0","source":{"id":"2604.08581","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.08581","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"arxiv_version","alias_value":"2604.08581v1","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.08581","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"pith_short_12","alias_value":"PP3CJZ3FP2QO","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"pith_short_16","alias_value":"PP3CJZ3FP2QOFG6Q","created_at":"2026-06-12T01:09:27Z"},{"alias_kind":"pith_short_8","alias_value":"PP3CJZ3F","created_at":"2026-06-12T01:09:27Z"}],"graph_snapshots":[{"event_id":"sha256:56111df5cc006f5417c50f2a2223f7752f4daf7ccb988bc664879c8887f07719","target":"graph","created_at":"2026-06-12T01:09:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB SRAM and 63 KB Flash."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That power side-channel RMS values under controlled anomaly conditions in the 14-day mini-fridge dataset are representative of real-world anomalies and that Z-score thresholds derived from the training phase will generalize without overfitting or missing subtle deviations."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A Z-score TinyML system trains and detects appliance anomalies entirely on microcontrollers using power side-channel data with perfect accuracy and minimal resources."}],"snapshot_sha256":"f91ed98faf4b2034ccf5c6c19bc815f227d38cb96395c16a7df2dc0aa6a63e46"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.08581/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents a fully autonomous Tiny Machine Learning (TinyML) Z-Score-based anomaly detection system deployed on a low-power microcontroller for real-time monitoring of appliance behavior using power side-channel data. Unlike existing Internet of Things (IoT) anomaly detection approaches that rely on offline training or cloud-assisted analytics, the proposed system performs both model training and inference directly on a resource-constrained microcontroller without external computation or connectivity. The system continuously samples current consumption, computes Root Mean Square (RMS)","authors_text":"Abdulrahman Albaiz, Fathi Amsaad","cross_cats":[],"headline":"A Z-score TinyML system trains and detects appliance anomalies entirely on microcontrollers using power side-channel data with perfect accuracy and minimal resources.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-28T20:15:50Z","title":"Fully Autonomous Z-Score-Based TinyML Anomaly Detection on Resource-Constrained MCUs Using Power Side-Channel Data"},"references":{"count":15,"internal_anchors":0,"resolved_work":15,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"A Comprehensive Survey on TinyML","work_id":"612c03a2-e5b9-4a1e-9970-d23b577d88f0","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"TinyML -Enabled Frugal Smart Objects: Challenges and Opportunities","work_id":"5bf738a1-3d32-497a-9a2d-dbdfa3cb01ae","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Benchmarking TinyML Systems: Challenges and Direction","work_id":"922e79c4-b1db-4146-9f0d-6904f483ea1d","year":2003},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Anomaly Detection in Smart Environments: A Comprehensive Survey","work_id":"49ab43d5-6541-4f12-8ced-6d5316474b79","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Nonintrusive Appliance Load Monitoring: Review and Outlook","work_id":"c5e627f4-dc54-4021-bffe-82d46b35c360","year":2011}],"snapshot_sha256":"1d32ffb9c9d22e948bcc7aeaed598d0b66c783059f53c50701c12cf883c18851"},"source":{"id":"2604.08581","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T22:05:30.422912Z","id":"1e41ad32-a0c2-4f1a-bd24-05389cb2d413","model_set":{"reader":"grok-4.3"},"one_line_summary":"A Z-score anomaly detector trained and inferred fully on an STM32 microcontroller using power side-channel RMS data achieves perfect precision and recall on a 14-day fridge dataset with low memory and latency.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A Z-score TinyML system trains and detects appliance anomalies entirely on microcontrollers using power side-channel data with perfect accuracy and minimal resources.","strongest_claim":"Results demonstrate perfect detection performance, with Precision and Recall of 1.00, inference latencies on the order of tens of microseconds, and a total memory footprint of approximately 3.3 KB SRAM and 63 KB Flash.","weakest_assumption":"That power side-channel RMS values under controlled anomaly conditions in the 14-day mini-fridge dataset are representative of real-world anomalies and that Z-score thresholds derived from the training phase will generalize without overfitting or missing subtle deviations."}},"verdict_id":"1e41ad32-a0c2-4f1a-bd24-05389cb2d413"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f04d79dc4fc5812bfad77b3227733958f8eb007880efdfe4b7cd7e2197d5f908","target":"record","created_at":"2026-06-12T01:09:27Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e80e9c38142e9ff7899e66cf87ab20aeaf86a1ef5ae795a8ec765155fff7621f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-28T20:15:50Z","title_canon_sha256":"cf92076f4c65e72a79ad833d692fb3d80fb87fea233987d56bd26871b21ec01f"},"schema_version":"1.0","source":{"id":"2604.08581","kind":"arxiv","version":1}},"canonical_sha256":"7bf624e7657ea0e29bd0fb06cc93efa0c76927e8562c41e2906ee5a8c6496358","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7bf624e7657ea0e29bd0fb06cc93efa0c76927e8562c41e2906ee5a8c6496358","first_computed_at":"2026-06-12T01:09:27.558787Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-12T01:09:27.558787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5TGxsjxkpq8AROetTzcOC+JL901VdS3mMwJ1isQ1EpbN1f8yMaO3jnX1ZdhCjIQkILPEPM+osZc3hXelgyqYCQ==","signature_status":"signed_v1","signed_at":"2026-06-12T01:09:27.559191Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.08581","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f04d79dc4fc5812bfad77b3227733958f8eb007880efdfe4b7cd7e2197d5f908","sha256:56111df5cc006f5417c50f2a2223f7752f4daf7ccb988bc664879c8887f07719"],"state_sha256":"71ac00a62aacb4819749ab557caee770866302ae3e21313d2649ac3ef61cccec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PvcCpbHxtHmJgmkaCP6hi5TKK/OhCF3WxiPwMxW+N2sQssQ+feN0mAfa5v0Pis76r8i74YNBtsp19uMI4zfqDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T12:32:15.797210Z","bundle_sha256":"d845b1ec376aa946a360f6674b82936f438e84bd6120df725ad6c8ac9b18f8af"}}